Where does the agent run?

In your own cloud account or on your premises, on foundation models your security team approves. Your data does not have to leave your environment.

How does it support compliance and audit?

Every action is logged with the reasoning behind it and the source it relied on, so audit and model-risk teams can see how a figure or term was reached.

What happens when the agent is unsure?

Steps below the confidence threshold you set go to a person before anything is updated or reported.

AI for financial services: audit trails for every conclusion

Industry · Financial services

We build agents with finance teams that reconcile figures and review documents, with each step recorded for compliance.

Talk to usSee Agentic implementation

Where it helps

Document review

Extract terms from contracts and filings, each tied to its clause.

Reconciliation

Explain every difference between two sets of figures, step by step.

Model risk

Flag low-confidence steps for human review before they reach a decision.

Example

A short example of the kind of trace this work produces.

Documents · Extraction

Question

Pull the renewal date and notice period from this contract.

Illustrative example, not live model output.

How we help

Agentic implementation

Agents that do real work, built on Lemma

Enterprise · For operations and product teams →

AI strategy

Where AI creates value for you, and what to do first

Enterprise · For leadership teams →

Questions

Talk to us

In your own cloud account or on your premises, on foundation models your security team approves. Your data does not have to leave your environment.

Illustrative scenario · Financial services

An operations team hands contract review to an agent

Read the scenario →

Every conclusion with an audit trail

What we work on

Document review

Reconciliation

Model risk

See the reasoning

Apps and services

Agentic implementation

AI strategy

Financial services: common questions